The past year has seen continued application of next-Gen sequencing approaches in studies that use human tissues from several clinical sources. We obtain cord blood monocytes from newborns, while monocytes from adults are available through the NIH Department of Transfusion Medicine. These cells are analyzed either directly or after differentiation in vitro into dendritic cells. We procure human skin fibroblasts from newborns and adults, as needed, under a protocol approved by the NICHD Institutional Review Board. Third, through collaborative work with Dr. Alan DeCherney (NICHD), we obtain human ovarian granulosa cells harvested in association with ART services at Shady Grove Hospital (Gaithersburg, MD). With both monocyte- and fibroblast-based experimental systems, we previously employed microarrays to study developmental and age-related changes in gene regulation. Recent experiments with granulosa cells have used RNA-seq to characterize gene expression, and RRBS (reduced representation bisulfite sequencing) to examine DNA methylation patterns. Our current work focuses on whole genome surveys to explore the epigenome features that underlie changes in gene function. In mammalian cells, cis-dependent epigenetic states are, of course, maintained by both chromatin structure and DNA methylation. Chromatin states are measured with respect to histone methylation and histone acetylation levels, as well as the topologies of the latter patterns extending over domains that typically encompass one or more genes. DNA methylation is characterized at the whole genome level (MethylCap-seq), as well as at nucleotide resolution within CpG-enriched regions (RRBS). Of particular interest to us are interactions between the chromatin- and DNA methylationbased components of epigenetic control. Given the rapid progress of epigenomics and the very large data sets generated by chromatin immunoprecipitation (ChIP) combined with next-Gen sequencingbased (ChIP-seq) analysescontinuing refinement of bioinformatics tools is absolutely essential. Along these lines, we constantly improve and extend our genome annotation, pattern recognition, and pattern comparison algorithms. Most recently, bioinformatic tools have been developed and applied to efficiently link patterns in RNA expression data sets with epigenome features. Further, a new approach focused on non-randomly clustered DNA methylation patterns (variegation, imprinting, and random monoallelic states) has revealed important new insights into age-related epigenome change. Chromatin-related results to date indicate that genes subject to both differentiation and developmental controls depend on the three-dimensional topology of the genome and are sensitive to the remodeling of higher-order chromatin structures. We will continue to focus on large (on the order of 100 kb) domains over which histone acetylation or histone H3-K27 patterns are altered, as well as on control elements that may assume nonB DNA conformations. DNA methylation-related results indicate that a little-studied subcompartment of the genome consisting of small sets of regions having high homology - either within gene clusters or dispersed gene families - may be substantially enriched for post-natal developmental- and age-related epigenome remodeling. If our hypothesis is correct, such regions may provide clock-like functions of considerable importance. The emerging goal is to generalize our paradigm of dynamic postnatal epigenome structure to address a range of current problems in maternal reproductive health, pediatrics and areas of medicine relating to age-related disease. Based on the genes currently under study, we will most likely investigate peripheral insulin resistance and diabetes in adolescents and young adults, and the contribution of granulosa cell aging to reduced ovarian function.
